Effects of forest disturbance on snow mass and energy balance in headwater regions

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Forests across western North America are dying as a result of drought, insect infestation, wildfire and disease. The resulting hydrologic impacts are particularly significant in snowmelt dominated headwater basins, where forests mediate snow-atmosphere interactions. Runoff from these watersheds can provide more than 80% of water supply for downstream agricultural, residential and industrial users. Snowmelt is particularly critical in semi-arid regions, such as southwestern Alberta and the southwestern US, where stored spring meltwater augments water supply during the dry summer season. Changing forest structure following disturbance affects snow accumulation and ablation, with cascading effects on downstream hydrology. The proposed research focuses on enhancing our ability to manage water supply from forested mountain headwaters given the effects of changing forest cover on snowpack mass and energy balance. While we partially understand the effects of forest disturbance on stand-scale snow hydrology, our knowledge remains limited to specific portions of the energy balance (e.g., shortwave radiation) and to specific scales (forest stand scale). Research is required to quantify the effects of disturbance on the longwave and turbulent flux components of the energy balance. This information can then be used in numerical models to better understand the effect of changing snowpack energetics on basin-scale runoff. This is the first research to focus specifically on: (1) the effects of forest disturbance on stand-scale snow accumulation variability; (2) the effects of dead trees on the longwave component of the energy balance; (3) the impacts of winter chinook events on turbulent fluxes at the below canopy snow surface; and (4) spatial patterns of snow accumulation and melt following disturbance. Examination of these processes across scales will allow results to be used to improve forest-water management tools such as hydrologic models. Improved model output can then be used by forest and water resource managers to make scientifically-based management decisions regarding maintenance of non-timber hydrologic values, and surface water resource allocations.